Abstract

Association rule discovery and classificationare common data mining tasks. Integrating associationrule and classification also known as associativeclassification is a promising approach that derivesclassifiers highly competitive with regards to accuracy tothat of traditional classification approaches such as ruleinduction and decision trees. However, the size of theclassifiers generated by associative classification is oftenlarge and therefore pruning becomes an essential task.In this paper, we survey different rule pruning methodsused by current associative classification techniques.Further, we compare the effect of three pruning methods(database coverage, pessimistic error estimation, lazypruning) on the accuracy rate and the number of rulesderived from different classification data sets. Resultsobtained from experimenting on different data sets fromUCI data collection indicate that lazy pruning algorithmsmay produce slightly higher predictive classifiers thanthose which utilise database coverage and pessimisticerror pruning methods. However, the potential use of suchclassifiers is limited because they are difficult tounderstand and maintain by the end-user.